


'''simple docstring'''







import argparse
import os

import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from torch import nn

from transformers import BertConfig, BertForMaskedLM, BertModel, RobertaTokenizer
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


if version.parse(nlp.__version__) != version.parse("""0.8.3"""):
       raise Exception("""requires gluonnlp == 0.8.3""")

if version.parse(mx.__version__) != version.parse("""1.5.0"""):
       raise Exception("""requires mxnet == 1.5.0""")

logging.set_verbosity_info()
lowerCAmelCase     :  str											=    logging.get_logger(__name__)

lowerCAmelCase     :  int											=    """The Nymphenburg Palace is a beautiful palace in Munich!"""






def       lowercase					(_A ,    _A       ):







       """simple docstring"""







       _lowerCAmelCase     :				Union[str, Any]       =							{
           'attention_cell': 'multi_head',
           'num_layers': 4,
           'units': 1_0_2_4,
           'hidden_size': 7_6_8,
           'max_length': 5_1_2,
           'num_heads': 8,
           'scaled': True,
           'dropout': 0.1,
           'use_residual': True,
           'embed_size': 1_0_2_4,
           'embed_dropout': 0.1,
           'word_embed': None,
           'layer_norm_eps': 1E-5,
           'token_type_vocab_size': 2,
       }

       _lowerCAmelCase     :				Union[str, Any]       =							bort_4_8_768_1024_hparams

       # Let's construct the original Bort model here
       # Taken from official BERT implementation, see:
       # https://github.com/alexa/bort/blob/master/bort/bort.py
       _lowerCAmelCase     :				Union[str, Any]       =							BERTEncoder(
           attention_cell=predefined_args['attention_cell'] ,    num_layers=predefined_args['num_layers'] ,    units=predefined_args['units'] ,    hidden_size=predefined_args['hidden_size'] ,    max_length=predefined_args['max_length'] ,    num_heads=predefined_args['num_heads'] ,    scaled=predefined_args['scaled'] ,    dropout=predefined_args['dropout'] ,    output_attention=_A ,    output_all_encodings=_A ,    use_residual=predefined_args['use_residual'] ,    activation=predefined_args.get('activation' ,    'gelu'       ) ,    layer_norm_eps=predefined_args.get('layer_norm_eps' ,    _A       ) ,    )

       # Vocab information needs to be fetched first
       # It's the same as RoBERTa, so RobertaTokenizer can be used later
       _lowerCAmelCase     :				List[Any]       =							'openwebtext_ccnews_stories_books_cased'

       # Specify download folder to Gluonnlp's vocab
       _lowerCAmelCase     :				List[str]       =							os.path.join(get_home_dir() ,    'models'       )
       _lowerCAmelCase     :				Optional[Any]       =							_load_vocab(_A ,    _A ,    _A ,    cls=_A       )

       _lowerCAmelCase     :				List[Any]       =							nlp.model.BERTModel(
           _A ,    len(_A       ) ,    units=predefined_args['units'] ,    embed_size=predefined_args['embed_size'] ,    embed_dropout=predefined_args['embed_dropout'] ,    word_embed=predefined_args['word_embed'] ,    use_pooler=_A ,    use_token_type_embed=_A ,    token_type_vocab_size=predefined_args['token_type_vocab_size'] ,    use_classifier=_A ,    use_decoder=_A ,    )

       original_bort.load_parameters(_A ,    cast_dtype=_A ,    ignore_extra=_A       )
       _lowerCAmelCase     :				Any       =							original_bort._collect_params_with_prefix()

       # Build our config 🤗
       _lowerCAmelCase     :				Dict       =							{
           'architectures': ['BertForMaskedLM'],
           'attention_probs_dropout_prob': predefined_args['dropout'],
           'hidden_act': 'gelu',
           'hidden_dropout_prob': predefined_args['dropout'],
           'hidden_size': predefined_args['embed_size'],
           'initializer_range': 0.02,
           'intermediate_size': predefined_args['hidden_size'],
           'layer_norm_eps': predefined_args['layer_norm_eps'],
           'max_position_embeddings': predefined_args['max_length'],
           'model_type': 'bort',
           'num_attention_heads': predefined_args['num_heads'],
           'num_hidden_layers': predefined_args['num_layers'],
           'pad_token_id': 1,  # 2 = BERT, 1 = RoBERTa
           'type_vocab_size': 1,  # 2 = BERT, 1 = RoBERTa
           'vocab_size': len(_A       ),
       }

       _lowerCAmelCase     :				str       =							BertConfig.from_dict(_A       )
       _lowerCAmelCase     :				Any       =							BertForMaskedLM(_A       )
       hf_bort_model.eval()

       # Parameter mapping table (Gluonnlp to Transformers)
       # * denotes layer index
       #
       # | Gluon Parameter                                                | Transformers Parameter
       # | -------------------------------------------------------------- | ----------------------
       # | `encoder.layer_norm.beta`                                      | `bert.embeddings.LayerNorm.bias`
       # | `encoder.layer_norm.gamma`                                     | `bert.embeddings.LayerNorm.weight`
       # | `encoder.position_weight`                                      | `bert.embeddings.position_embeddings.weight`
       # | `word_embed.0.weight`                                          | `bert.embeddings.word_embeddings.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_key.bias`     | `bert.encoder.layer.*.attention.self.key.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_key.weight`   | `bert.encoder.layer.*.attention.self.key.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_query.bias`   | `bert.encoder.layer.*.attention.self.query.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_query.weight` | `bert.encoder.layer.*.attention.self.query.weight`
       # | `encoder.transformer_cells.*.attention_cell.proj_value.bias`   | `bert.encoder.layer.*.attention.self.value.bias`
       # | `encoder.transformer_cells.*.attention_cell.proj_value.weight` | `bert.encoder.layer.*.attention.self.value.weight`
       # | `encoder.transformer_cells.*.ffn.ffn_2.bias`                   | `bert.encoder.layer.*.attention.output.dense.bias`
       # | `encoder.transformer_cells.*.ffn.ffn_2.weight`                 | `bert.encoder.layer.*.attention.output.dense.weight`
       # | `encoder.transformer_cells.*.layer_norm.beta`                  | `bert.encoder.layer.*.attention.output.LayerNorm.bias`
       # | `encoder.transformer_cells.*.layer_norm.gamma`                 | `bert.encoder.layer.*.attention.output.LayerNorm.weight`
       # | `encoder.transformer_cells.*.ffn.ffn_1.bias`                   | `bert.encoder.layer.*.intermediate.dense.bias`
       # | `encoder.transformer_cells.*.ffn.ffn_1.weight`                 | `bert.encoder.layer.*.intermediate.dense.weight`
       # | `encoder.transformer_cells.*.ffn.layer_norm.beta`              | `bert.encoder.layer.*.output.LayerNorm.bias`
       # | `encoder.transformer_cells.*.ffn.layer_norm.gamma`             | `bert.encoder.layer.*.output.LayerNorm.weight`
       # | `encoder.transformer_cells.*.proj.bias`                        | `bert.encoder.layer.*.output.dense.bias`
       # | `encoder.transformer_cells.*.proj.weight`                      | `bert.encoder.layer.*.output.dense.weight`

       # Helper function to convert MXNET Arrays to PyTorch
       def to_torch(_A       ) -> nn.Parameter:
              return nn.Parameter(torch.FloatTensor(mx_array.data().asnumpy()       )       )

       # Check param shapes and map new HF param back
       def check_and_map_params(_A ,    _A       ):
              _lowerCAmelCase     :				str       =							hf_param.shape

              _lowerCAmelCase     :				Optional[int]       =							to_torch(params[gluon_param]       )
              _lowerCAmelCase     :				str       =							gluon_param.shape

              assert (
                  shape_hf == shape_gluon
              ), f'The gluon parameter {gluon_param} has shape {shape_gluon}, but expects shape {shape_hf} for Transformers'

              return gluon_param

       _lowerCAmelCase     :				Any       =							check_and_map_params(
           hf_bort_model.bert.embeddings.word_embeddings.weight ,    'word_embed.0.weight'       )
       _lowerCAmelCase     :				Any       =							check_and_map_params(
           hf_bort_model.bert.embeddings.position_embeddings.weight ,    'encoder.position_weight'       )
       _lowerCAmelCase     :				Any       =							check_and_map_params(
           hf_bort_model.bert.embeddings.LayerNorm.bias ,    'encoder.layer_norm.beta'       )
       _lowerCAmelCase     :				Optional[Any]       =							check_and_map_params(
           hf_bort_model.bert.embeddings.LayerNorm.weight ,    'encoder.layer_norm.gamma'       )

       # Inspired by RoBERTa conversion script, we just zero them out (Bort does not use them)
       _lowerCAmelCase     :				Dict       =							torch.zeros_like(
           hf_bort_model.bert.embeddings.token_type_embeddings.weight.data       )

       for i in range(hf_bort_config.num_hidden_layers       ):
              _lowerCAmelCase     :				Union[str, Any]       =							hf_bort_model.bert.encoder.layer[i]

              # self attention
              _lowerCAmelCase     :				Optional[int]       =							layer.attention.self

              _lowerCAmelCase     :				Dict       =							check_and_map_params(
                  self_attn.key.bias.data ,    f'encoder.transformer_cells.{i}.attention_cell.proj_key.bias'       )

              _lowerCAmelCase     :				int       =							check_and_map_params(
                  self_attn.key.weight.data ,    f'encoder.transformer_cells.{i}.attention_cell.proj_key.weight'       )
              _lowerCAmelCase     :				Dict       =							check_and_map_params(
                  self_attn.query.bias.data ,    f'encoder.transformer_cells.{i}.attention_cell.proj_query.bias'       )
              _lowerCAmelCase     :				List[str]       =							check_and_map_params(
                  self_attn.query.weight.data ,    f'encoder.transformer_cells.{i}.attention_cell.proj_query.weight'       )
              _lowerCAmelCase     :				str       =							check_and_map_params(
                  self_attn.value.bias.data ,    f'encoder.transformer_cells.{i}.attention_cell.proj_value.bias'       )
              _lowerCAmelCase     :				str       =							check_and_map_params(
                  self_attn.value.weight.data ,    f'encoder.transformer_cells.{i}.attention_cell.proj_value.weight'       )

              # self attention output
              _lowerCAmelCase     :				Tuple       =							layer.attention.output

              _lowerCAmelCase     :				Union[str, Any]       =							check_and_map_params(
                  self_output.dense.bias ,    f'encoder.transformer_cells.{i}.proj.bias'       )
              _lowerCAmelCase     :				Optional[int]       =							check_and_map_params(
                  self_output.dense.weight ,    f'encoder.transformer_cells.{i}.proj.weight'       )
              _lowerCAmelCase     :				str       =							check_and_map_params(
                  self_output.LayerNorm.bias ,    f'encoder.transformer_cells.{i}.layer_norm.beta'       )
              _lowerCAmelCase     :				Tuple       =							check_and_map_params(
                  self_output.LayerNorm.weight ,    f'encoder.transformer_cells.{i}.layer_norm.gamma'       )

              # intermediate
              _lowerCAmelCase     :				Any       =							layer.intermediate

              _lowerCAmelCase     :				Dict       =							check_and_map_params(
                  intermediate.dense.bias ,    f'encoder.transformer_cells.{i}.ffn.ffn_1.bias'       )
              _lowerCAmelCase     :				Optional[Any]       =							check_and_map_params(
                  intermediate.dense.weight ,    f'encoder.transformer_cells.{i}.ffn.ffn_1.weight'       )

              # output
              _lowerCAmelCase     :				Optional[int]       =							layer.output

              _lowerCAmelCase     :				int       =							check_and_map_params(
                  bert_output.dense.bias ,    f'encoder.transformer_cells.{i}.ffn.ffn_2.bias'       )
              _lowerCAmelCase     :				Dict       =							check_and_map_params(
                  bert_output.dense.weight ,    f'encoder.transformer_cells.{i}.ffn.ffn_2.weight'       )
              _lowerCAmelCase     :				Optional[int]       =							check_and_map_params(
                  bert_output.LayerNorm.bias ,    f'encoder.transformer_cells.{i}.ffn.layer_norm.beta'       )
              _lowerCAmelCase     :				Optional[Any]       =							check_and_map_params(
                  bert_output.LayerNorm.weight ,    f'encoder.transformer_cells.{i}.ffn.layer_norm.gamma'       )

       # Save space and energy 🎄
       hf_bort_model.half()

       # Compare output of both models
       _lowerCAmelCase     :				List[str]       =							RobertaTokenizer.from_pretrained('roberta-base'       )

       _lowerCAmelCase     :				Union[str, Any]       =							tokenizer.encode_plus(_A       )['input_ids']

       # Get gluon output
       _lowerCAmelCase     :				List[str]       =							mx.nd.array([input_ids]       )
       _lowerCAmelCase     :				Optional[Any]       =							original_bort(inputs=_A ,    token_types=[]       )

       # Get Transformer output (save and reload model again)
       hf_bort_model.save_pretrained(_A       )
       _lowerCAmelCase     :				List[Any]       =							BertModel.from_pretrained(_A       )
       hf_bort_model.eval()

       _lowerCAmelCase     :				int       =							tokenizer.encode_plus(_A ,    return_tensors='pt'       )
       _lowerCAmelCase     :				Optional[Any]       =							hf_bort_model(**_A       )[0]

       _lowerCAmelCase     :				Any       =							output_gluon[0].asnumpy()
       _lowerCAmelCase     :				Dict       =							output_hf[0].detach().numpy()

       _lowerCAmelCase     :				Optional[Any]       =							np.max(np.abs(hf_layer - gluon_layer       )       ).item()
       _lowerCAmelCase     :				Union[str, Any]       =							np.allclose(_A ,    _A ,    atol=1E-3       )

       if success:
              print('✔️ Both model do output the same tensors'       )
       else:
              print('❌ Both model do **NOT** output the same tensors'       )
              print('Absolute difference is:' ,    _A       )


if __name__ == "__main__":
       lowerCAmelCase     :  List[Any]											=    argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           """--bort_checkpoint_path""", default=None, type=str, required=True, help="""Path the official Bort params file."""
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
       )
       lowerCAmelCase     :  Any											=    parser.parse_args()
       convert_bort_checkpoint_to_pytorch(args.bort_checkpoint_path, args.pytorch_dump_folder_path)





from unittest.mock import Mock, patch

from file_transfer.send_file import send_file
@patch("socket.socket"	)
@patch("builtins.open"	)
def 							UpperCamelCase_(					_A :Tuple					,  _A :str	)->		int:
			# ===== initialization =====
			UpperCamelCase__       =							Mock()
			UpperCamelCase__       =							conn, Mock()
			UpperCamelCase__       =							iter([1, None]	)
			UpperCamelCase__       =							lambda _A	: next(_A	)

			# ===== invoke =====
			send_file(filename="mytext.txt"					,  testing=_A	)

			# ===== ensurance =====
			sock.assert_called_once()
			sock.return_value.bind.assert_called_once()
			sock.return_value.listen.assert_called_once()
			sock.return_value.accept.assert_called_once()
			conn.recv.assert_called_once()

			file.return_value.__enter__.assert_called_once()
			file.return_value.__enter__.return_value.read.assert_called()

			conn.send.assert_called_once()
			conn.close.assert_called_once()
			sock.return_value.shutdown.assert_called_once()
			sock.return_value.close.assert_called_once()


